The Application of Supervised and Unsupervised Computational Predictive Models to Simulate the COVID19 Pandemic-Reference-Cited by-同舟云学术

The Application of Supervised and Unsupervised Computational Predictive Models to Simulate the COVID19 Pandemic

Published:2022 Issue: Volume: Page:103-139
ISSN:
Container-title:Epidemic Analytics for Decision Supports in COVID19 Crisis
language:
Short-container-title:

Author:

Fong Simon James,Marques João Alexandre Lobo,Li Gloria,Dey Nilanjan,Crespo Rubén González,Herrera-Viedma Enrique,Gois Francisco Nauber Bernardo,Neto José Xavier

Publisher

Springer International Publishing

Link

https://link.springer.com/content/pdf/10.1007/978-3-030-95281-5_5

Reference24 articles.

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2. N. Madhav, B. Oppenheim, M. Gallivan, et al, Pandemics: risks, impacts, and mitigation, in Disease Control Priorities: Improving Health and Reducing Poverty, 3rd edn, ed. by D.T. Jamison, H. Gelband, S. Horton, et al. Washington (DC): The International Bank for Reconstruction and Development/The World Bank; 2017 Nov 27. Chapter 17. Available from: https://www.ncbi.nlm.nih.gov/books/NBK525302/ https://doi.org/10.1596/978-1-4648-0527-1_ch17

3. S.J. Fong, G. Li, N. Dey, R.G. Crespo, E. Herrera-Viedma, Composite Monte Carlo decision making under high uncertainty of novel coronavirus epidemic using hybridized deep learning and fuzzy rule induction. Appl. Soft Comput. 93, 106282 (2020). Advance online publication. https://doi.org/10.1016/j.asoc.2020.106282

4. C.I. Siettos, L. Russo, Mathematical modeling of infectious disease dynamics. Virulence 4(4), 295–306 (2013). https://doi.org/10.4161/viru.24041

5. H.M. Singer, The COVID19 pandemic: growth patterns, power law scaling, and saturation. Phys. Biol. 17(5), 055001 (2020). Published 2020 Jul 29. https://doi.org/10.1088/1478-3975/ab9bf5